One of the major findings of the U.S. Department of Energy's “Grid 2030” strategy is that “America's electric system, ‘the supreme engineering achievement of the 20th century’ is aging, inefficient, congested, incapable of meeting the future energy needs [ . . . ].” Reliability will be a key issue as electrical grids transform throughout the next several decades, and grid maintenance will become even more critical than it is currently. A 2007 survey by the NERC stated that “aging infrastructure and limited new construction” is the largest challenge to electrical grid reliability out of all challenges considered by the survey. The Smart Grid will bring operations and maintenance more online—moving the industry from reactive to proactive operations. Power companies keep historical data records regarding equipment and past failures, but those records are generally not being used to their full extent for predictive maintenance and assisting grid reliability.
Most power grids in U.S. cities (e.g., electrical grids in the Northeast and other mature cities) have been built gradually over the last 120 years. This means that the electrical equipment (transformers, cables, joints, terminators, and associated switches, network protectors, relays, etc.) vary in age; for instance, at least 5% of the low voltage cables in Manhattan were installed before 1930, and a few of the original high voltage feeder sections installed during the Thomas Edison era are still in active use in NYC. In NYC there are over 94,000 miles of high voltage underground distribution cable, enough to wrap around the earth three and a half times. Boston has 3,000 miles of underground cable and many other cities have similarly large underground electric systems.
Maintaining a large grid that is a mix of new and old components is more difficult than managing a new grid (for instance, as is being laid in some parts of China). The U.S. grid is generally older than many European grids that were replaced after WWII, and older than grids in places where infrastructure must be continually replenished due to natural disasters (for instance, Japan has earthquakes that force power systems to be replenished).
The Smart Grid will not be implemented overnight. For instance, according to the Brattle Group, the cost of updating the grid by 2030 could be as much as $1.5 trillion. The major components of the Smart Grid will (for an extended period) be the same as the major components of the current grid, and new intelligent meters must work with the existing equipment. Converting to a Smart Grid has been compared to “replacing worn parts of a 747 while it's in the air.” To create the Smart Grid of the future, one must work with the electric grid that is there now. As grid parts are replaced gradually and as smart components are added, the old components, including cables, switches, sensors, etc., will still need to be maintained. Further, the state of the old components should dictate priorities for the addition of new smart switches and sensors, particularly in the secondary network.
The key to making Smart Grid components effective is to analyze where upgrades would be most useful, given the current system. Consider the analogy to human patients in the medical profession, a discipline for which many of the machine learning algorithms and techniques used for the Smart Grid were originally developed and tested. While each patient is made up of the same kinds of components (analogous to feeders, transformers, manholes, and joints), they wear and age differently, with variable historic stresses and hereditary factors (analogous to different vintages, loads, manufacturers) so that each patient must be treated as a unique individual. Nonetheless individuals group into families, neighborhoods, and populations (analogous to feeders, networks, boroughs) with relatively similar properties. The Smart Grid must be built upon a foundation of helping the electrical grid components (patients) improve their health, so that the networks (neighborhoods) improve their life expectancy, and the population (boroughs) lives more sustainably.
A need exists for proactive predictive maintenance programs for electrical grid reliability. There is also a need to make use of existing data resources, including data resources that were not originally obtained or designed for predictive purposes (e.g., maintenance record or a maintenance requests).